KDD: Knowledge Discovery and Data Mining (KDD) Insititute: 复旦大学,中科大 Problem: time series prediction; modelling extreme events; overlook the existence of extreme events, which result in weak performance when applying them to real time series. 为什么研究ext…

Problem: time series prediction The nonlinear autoregressive exogenous model: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values…

LSTM NEURAL NETWORK FOR TIME SERIES PREDICTION Wed 21st Dec 2016   Neural Networks these days are the "go to" thing when talking about new fads in machine learning. As such, there's a plethora of courses and tutorials out there on the basic vani…

LSTM Neural Network for Time Series Prediction Wed 21st Dec 2016 Neural Networks these days are the “go to” thing when talking about new fads in machine learning. As such, there’s a plethora of courses and tutorials out there on the basic vanilla neu…

However: The conventional visual mapping maps each data attribute onto a single visual channel Purpose: investigate composite visual mapping综合可视映射: mapping single data attributes onto several visual channels. a valuable tool for understanding those r…

Problem: multi-horizon probabilistic forecasting tasks; Propose an end-to-end framework for multi-horizon time series forecasting, with temporal attention mechanisms to capture latent patterns. Introduction: forecasting ----- understanding demands. t…

Improvement can be done in fulture:1. the algorithm of constructing network from distance matrix. 2. evolution of sliding time window3. the later processing or visual analysis of generated graphs. Thinking: 1.What's the ground truth in load profiles?…

Why Time Series Data Is Unique A time series is a series of data points indexed in time. The fact that time series data is ordered makes it unique in the data space because it often displays serial dependence序列依赖. Serial dependence occurs when the va…

原文地址:https://cn.mathworks.com/help/fuzzy/examples/chaotic-time-series-prediction.html?requestedDomain=www.mathworks.com This example shows how to do chaotic time series prediction using ANFIS. Time Series Data The data is generated from the Mackey-Gl…

Problem: new loss Label: new loss; Abstract: A differentiable learning loss; Introduction: supervised learning: learn a mapping that links an input to an output object. output object is a time series. Prediction: two multi-layer perceptrons, the firs…

  欢迎关注博主主页,学习python视频资源,还有大量免费python经典文章 python风控评分卡建模和风控常识 https://study.163.com/course/introduction.htm?courseId=1005214003&utm_campaign=commission&utm_source=cp-400000000398149&utm_medium=share Toby,项目合作QQ:231469242 Credit Scorecards – Intr…

Conferences ACM SEACM Southeast Regional Conference ACM Southeast Regional Conference the oldest, continuously running, annual conference of the ACM. ACMSE provides an excellent forum for both faculty and students to present their research in a frien…

Accepted Papers by Session Research Session RT01: Social and Graphs 1Tuesday 10:20 am–12:00 pm | Level 3 – Ballroom AChair: Tanya Berger-Wolf Efficient Algorithms for Public-Private Social NetworksFlavio Chierichetti,Sapienza University of Rome; Ales…

Awesome Courses  Introduction There is a lot of hidden treasure lying within university pages scattered across the internet. This list is an attempt to bring to light those awesome courses which make their high-quality material i.e. assignments, lect…

Time Series Anomaly Detection in Network Traffic: A Use Case for Deep Neural Networks from:https://jask.com/time-series-anomaly-detection-in-network-traffic-a-use-case-for-deep-neural-networks/ Introduction As the waves of the big data revolution cas…

2014大会记" title="史无前例的KDD 2014大会记"> 作者:蒋朦 微软亚洲研究院实习生 创造多项纪录的KDD 2014 ACM SIGKDD 国际会议(简称KDD)是由ACM的知识发现及数据挖掘专委会(SIGKDD)主办的数据挖掘研究领域的顶级年会.KDD 2014于8月24日至27日在美国纽约召开.正值大会的20岁生日,今年的KDD创造了多项的纪录,令参会者们印象深刻: 一. 史无前例的"超级大会":参会人员突破2200人.提前售完…

翻译来自:http://news.csdn.net/article_preview.html?preview=1&reload=1&arcid=2825492 摘要:本文解释了回归分析及其优势,重点总结了应该掌握的线性回归.逻辑回归.多项式回归.逐步回归.岭回归.套索回归.ElasticNet回归等七种最常用的回归技术及其关键要素,最后介绍了选择正确的回归模型的关键因素. [编者按]回归分析是建模和分析数据的重要工具.本文解释了回归分析的内涵及其优势,重点总结了应该掌握的线性回归.逻辑回归…

0.引言 我们发现传统的(如前向网络等)非循环的NN都是假设样本之间无依赖关系(至少时间和顺序上是无依赖关系),而许多学习任务却都涉及到处理序列数据,如image captioning,speech synthesis,music generation是基于模型输出序列数据:如time series prediction,video analysis,musical information retrieval是基于模型输入需要序列数据:而如translating natural language…

python信用评分卡(附代码,博主录制) https://study.163.com/course/introduction.htm?courseId=1005214003&utm_campaign=commission&utm_source=cp-400000000398149&utm_medium=share 变量筛选Variables Selection in Predictive Analytics Predictive Analytics: Variables Sele…

补充:https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-276 如果用arima的话,还不如使用随机森林... 原文地址:https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-9-time-series-analysis-in-python-a270cb05e0b3 数据集样子: y ti…

ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizona, USA ICLR 2013 Workshop Track Accepted for Oral Presentation Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sr…

https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ What is Regression Analysis? Why do we use Regression Analysis? What are the types of Regressions? Linear Regression Logistic Regression Polynomial Regression Stepwise Regre…

时间序列模型 时间序列预测分析就是利用过去一段时间内某事件时间的特征来预测未来一段时间内该事件的特征.这是一类相对比较复杂的预测建模问题,和回归分析模型的预测不同,时间序列模型是依赖于事件发生的先后顺序的,同样大小的值改变顺序后输入模型产生的结果是不同的. 举个栗子:根据过去两年某股票的每天的股价数据推测之后一周的股价变化:根据过去2年某店铺每周想消费人数预测下周来店消费的人数等等 RNN 和 LSTM 模型 时间序列模型最常用最强大的的工具就是递归神经网络(recurrent neural n…

我对rabbitmq学习还不深入,这些翻译仅仅做资料保存,希望不要误导大家. There’s a dirty secret about creating queues and exchanges in Rabbit: by default they don’t survive reboot. That’s right; restart your RabbitMQ server and watch those queues and exchanges go poof (along with the…

在深度学习领域,传统的多层感知机(MLP)具有出色的表现,取得了许多成功,它曾在许多不同的任务上——包括手写数字识别和目标分类上创造了记录.甚至到了今天,MLP在解决分类任务上始终都比其他方法要略胜一筹.尽管如此,大多数专家还是会达成共识:MLP可以实现的功能仍然相当有限.究其原因,人类的大脑有着惊人的计算功能,而“分类”任务仅仅是其中很小的一个组成部分.我们不仅能够识别个体案例,更能分析输入信息之间的整体逻辑序列.这些信息序列富含有大量的内容,信息彼此间有着复杂的时间关联性,并且信息长度各种各…

Problem H. Horrible Truth Time Limit: 1 Sec Memory Limit: 256 MB 题目连接 http://codeforces.com/gym/100610 Description In a Famous TV Show “Find Out” there are n characters and only one Horrible Truth. To make the series breathtaking all way long, the sc…

InfluxDB 是一个开源分布式时序.事件和指标数据库.使用 Go 语言编写,无需外部依赖.其设计目标是实现分布式和水平伸缩扩展. 特点 schemaless(无结构),可以是任意数量的列 Scalable min, max, sum, count, mean, median 一系列函数,方便统计 Native HTTP API, 内置http支持,使用http读写 Powerful Query Language 类似sql Built-in Explorer 自带管理工具 管理界面: API…

Andrew Kirillov 著 Conmajia 译 2019 年 1 月 12 日 原文发表于 CodeProject(2006 年 11 月 19 日),已获作者本人授权. 本文介绍了一个用于神经网络计算的 C# 库,并展示了如何用这个函数库进行问题求解. 全文约 3700 字,建议阅读时间 8 分钟.原文是 13 年前的旧文章,这篇译文也是我早期学英语的习作.胜在题材选的好,人工智能基础研究,放到今天不仅没有过时,反而正是风头十足的前沿科技. 这个库最终命名为 ANNT(Artific…

Analysis Services (servers) Feature Enterprise Standard Web Express with Advanced Services Express with Tools Express Developer Scalable shared databases Yes           Yes Backup/Restore & Attach/Detach databases Yes Yes         Yes Synchronize datab…

目录 1. MTL的定义 2. MTL的机制 2.1. Representation Bias 2.2. Uncorrelated Tasks May Help? 3. MTL的用途 3.1. Using the Future to Predict the Present 3.2. Time Series Prediction 3.3. Using Extra Tasks to Focus Attention 3.4. Quantization Smoothing 3.5. Some Input…